code: def trainer_cfg():
      trainer_cfg = {
          "devices": 1,
          "num_nodes": 1,
          "accelerator": "gpu",
          "precision": 16,
          "logger": False,
          "enable_checkpointing": False,
          "use_distributed_sampler": False,
          "max_epochs": 1000,
          "max_steps": 100000,
          "log_every_n_steps": 10,
          "val_check_interval": 100,
          "limit_val_batches": 50,
          "limit_test_batches": 500,
          "accumulate_grad_batches": 1,
          "gradient_clip_val": 1.0,
      }
  
      return trainer_cfg
  
file_path: NeMo/tests/collections/nlp/test_gpt_model.py
imports: import os
  import pytest
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids
  from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
label_desc: Use image node_id 2 for calling a global function with example usage: trainer_cfg() and returns: trainer_cfg